lucidrains/egnn-pytorch

import torch
from egnn_pytorch import EGNN

layer1 = EGNN(dim = 512)
layer2 = EGNN(dim = 512)

feats = torch.randn(1, 16, 512)
coors = torch.randn(1, 16, 3)

feats, coors = layer1(feats, coors)
feats, coors = layer2(feats, coors) # (1, 16, 512), (1, 16, 3)

import torch
from egnn_pytorch import EGNN

layer1 = EGNN(dim = 512, edge_dim = 4)
layer2 = EGNN(dim = 512, edge_dim = 4)

feats = torch.randn(1, 16, 512)
coors = torch.randn(1, 16, 3)
edges = torch.randn(1, 16, 16, 4)

feats, coors = layer1(feats, coors, edges)
feats, coors = layer2(feats, coors, edges) # (1, 16, 512), (1, 16, 3)

import torch
from egnn_pytorch import EGNN_Network

net = EGNN_Network(
    num_tokens = 21,
    num_positions = 1024,           # unless what you are passing in is an unordered set, set this to the maximum sequence length
    dim = 32,
    depth = 3,
    num_nearest_neighbors = 8,
    coor_weights_clamp_value = 2.   # absolute clamped value for the coordinate weights, needed if you increase the num neareest neighbors
)

feats = torch.randint(0, 21, (1, 1024)) # (1, 1024)
coors = torch.randn(1, 1024, 3)         # (1, 1024, 3)
mask = torch.ones_like(feats).bool()    # (1, 1024)

feats_out, coors_out = net(feats, coors, mask = mask) # (1, 1024, 32), (1, 1024, 3)

import torch
from egnn_pytorch import EGNN_Network

net = EGNN_Network(
    num_tokens = 21,
    dim = 32,
    depth = 3,
    only_sparse_neighbors = True
)

feats = torch.randint(0, 21, (1, 1024))
coors = torch.randn(1, 1024, 3)
mask = torch.ones_like(feats).bool()

# naive adjacency matrix
# assuming the sequence is connected as a chain, with at most 2 neighbors - (1024, 1024)
i = torch.arange(1024)
adj_mat = (i[:, None] >= (i[None, :] - 1)) & (i[:, None] <= (i[None, :] + 1))

feats_out, coors_out = net(feats, coors, mask = mask, adj_mat = adj_mat) # (1, 1024, 32), (1, 1024, 3)

import torch
from egnn_pytorch import EGNN_Network

net = EGNN_Network(
    num_tokens = 21,
    dim = 32,
    depth = 3,
    num_adj_degrees = 3,           # fetch up to 3rd degree neighbors
    adj_dim = 8,                   # pass an adjacency degree embedding to the EGNN layer, to be used in the edge MLP
    only_sparse_neighbors = True
)

feats = torch.randint(0, 21, (1, 1024))
coors = torch.randn(1, 1024, 3)
mask = torch.ones_like(feats).bool()

# naive adjacency matrix
# assuming the sequence is connected as a chain, with at most 2 neighbors - (1024, 1024)
i = torch.arange(1024)
adj_mat = (i[:, None] >= (i[None, :] - 1)) & (i[:, None] <= (i[None, :] + 1))

feats_out, coors_out = net(feats, coors, mask = mask, adj_mat = adj_mat) # (1, 1024, 32), (1, 1024, 3)

import torch
from egnn_pytorch import EGNN_Network

net = EGNN_Network(
    num_tokens = 21,
    dim = 32,
    depth = 3,
    edge_dim = 4,
    num_nearest_neighbors = 3
)

feats = torch.randint(0, 21, (1, 1024))
coors = torch.randn(1, 1024, 3)
mask = torch.ones_like(feats).bool()

continuous_edges = torch.randn(1, 1024, 1024, 4)

# naive adjacency matrix
# assuming the sequence is connected as a chain, with at most 2 neighbors - (1024, 1024)
i = torch.arange(1024)
adj_mat = (i[:, None] >= (i[None, :] - 1)) & (i[:, None] <= (i[None, :] + 1))

feats_out, coors_out = net(feats, coors, edges = continuous_edges, mask = mask, adj_mat = adj_mat) # (1, 1024, 32), (1, 1024, 3)

import torch
from egnn_pytorch import EGNN_Network

net = EGNN_Network(
    num_tokens = 21,
    dim = 32,
    depth = 3,
    num_nearest_neighbors = 32,
    norm_coors = True,              # normalize the relative coordinates
    coor_weights_clamp_value = 2.   # absolute clamped value for the coordinate weights, needed if you increase the num neareest neighbors
)

feats = torch.randint(0, 21, (1, 1024)) # (1, 1024)
coors = torch.randn(1, 1024, 3)         # (1, 1024, 3)
mask = torch.ones_like(feats).bool()    # (1, 1024)

feats_out, coors_out = net(feats, coors, mask = mask) # (1, 1024, 32), (1, 1024, 3)

import torch
from egnn_pytorch import EGNN

model = EGNN(
    dim = dim,                         # input dimension
    edge_dim = 0,                      # dimension of the edges, if exists, should be > 0
    m_dim = 16,                        # hidden model dimension
    fourier_features = 0,              # number of fourier features for encoding of relative distance - defaults to none as in paper
    num_nearest_neighbors = 0,         # cap the number of neighbors doing message passing by relative distance
    dropout = 0.0,                     # dropout
    norm_feats = False,                # whether to layernorm the features
    norm_coors = False,                # whether to normalize the coordinates, using a strategy from the SE(3) Transformers paper    
    update_feats = True,               # whether to update features - you can build a layer that only updates one or the other
    update_coors = True,               # whether ot update coordinates
    only_sparse_neighbors = False,     # using this would only allow message passing along adjacent neighbors, using the adjacency matrix passed in 
    valid_radius = float('inf'),       # the valid radius each node considers for message passing
    m_pool_method = 'sum',             # whether to mean or sum pool for output node representation
    soft_edges = False,                # extra GLU on the edges, purportedly helps stabilize the network in updated version of the paper
    coor_weights_clamp_value = None    # clamping of the coordinate updates, again, for stabilization purposes
)